Department of Statistical Sciences, University of Padova, Italy
Abstract
We develop a new class of flexible replicated measurement error models (RMEM) based on the normal two-piece scale mixture (TP-SMN) family to model the distribution of the latent variable. In the proposed approach, the replicated observations are jointly modeled by a mixture of two components from a scale mixture skew-normal (SMSN) density. The flexibility of this class can enable the simultaneous accommodation of skewness, outliers, and multimodality. The proposed connection between the unobserved covariates and the response facilitates the construction of an EM-type algorithm to perform maximum likelihood estimation. The effectiveness of the maximum likelihood estimations is studied through the simulation studies. Also, the method is applied to analyze continuing survey data on food intake by individuals on diet habits.
Kahrari, F. (2024). A two-piece scale mixture normal measurement error models for replicated data. Stochastic Models in Probability and Statistics, 1(2), 211-228.
MLA
Fereshteh Kahrari. "A two-piece scale mixture normal measurement error models for replicated data", Stochastic Models in Probability and Statistics, 1, 2, 2024, 211-228.
HARVARD
Kahrari, F. (2024). 'A two-piece scale mixture normal measurement error models for replicated data', Stochastic Models in Probability and Statistics, 1(2), pp. 211-228.
VANCOUVER
Kahrari, F. A two-piece scale mixture normal measurement error models for replicated data. Stochastic Models in Probability and Statistics, 2024; 1(2): 211-228.
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